Senior Quantitative Developer

1 day ago

Minneapolis, Minnesota, United States Selby Jennings Full time

Develop advanced machine learning platforms and infrastructure to support model development, backtesting, and deployment for quantitative strategies. Collaborate with quants, data scientists, and engineering teams to deliver scalable, high-performance solutions that support algorithmic trading and financial analytics.

Key Responsibilities:

  • Design, build, and maintain the infrastructure and tools that enable large-scale ML model development, testing, and deployment.
  • Implement, optimize, and deploy machine learning models within the quant research environment.
  • Develop and manage data pipelines for high-frequency and low-latency financial data, enabling efficient data retrieval, transformation, and storage.
  • Fine-tune ML model and platform performance to optimize resource usage, processing speed, and model accuracy in a production environment.
  • Build and maintain robust backtesting frameworks, supporting the research and validation of models across various trading strategies and asset classes.
  • Implement tools and processes to assess and monitor model risk, ensuring compliance with regulatory requirements and internal standards.
  • Document platform architecture, model development workflows, and operational processes; promote software engineering best practices, including testing, version control, and code review.
  • Provide technical guidance to junior developers and collaborate across teams to share insights, drive innovation, and achieve strategic objectives.

Requirements:

  • 5+ years in quantitative development, financial software engineering, or similar, with a strong focus on ML infrastructure or platforms.
  • Expertise in Python, C++, or Java; strong knowledge of ML libraries (TensorFlow, PyTorch, Scikit-Learn) and quantitative libraries (NumPy, Pandas).
  • Solid understanding of financial markets, asset classes, and quantitative finance principles, including statistical analysis, time series modeling, and risk management.
  • Experience with distributed systems, high-performance computing, and parallel processing, ideally for large-scale data processing or model training.
  • Proficiency in working with SQL and NoSQL databases, ETL pipelines, and big data technologies (e.g., Spark, Hadoop) to support data-driven ML applications.
  • Hands-on experience with MLOps tools (e.g., Docker, Kubernetes, Airflow) and cloud platforms (AWS, GCP, or Azure) for model deployment and monitoring.
  • Strong analytical thinking, with a proactive approach to complex problem-solving in a fast-paced environment.
  • Bachelor's or Master's degree in Computer Science, Engineering, Financial Engineering, or a related field; CFA, CQF, or similar certifications are a plus.

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